Spatiotemporal identification of roadkill probability and systematic conservation planning

Lin, Yu‐Pin; Anthony, Johnathen; Lin, Wei-Chih; Lien, Wan‐Ching; Petway, Joy R.; Lin, Teng-Chiu

doi:10.1007/s10980-019-00807-w

Cited by 23 publications

(16 citation statements)

References 72 publications

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“…Like snakes, anurans, lizards and so on, most susceptible to negative road impacts (Brehme et al 2018). Based on the independent roadkill data, more than 70% of roadkill events occurred within the top 30% priority segments (Lin et al 2019). Furthermore, significant positive correlation can be found between the fractal dimension of forest stands and road density across all scales (Miller et al 1996).…”

Section: Implications For Forest Road Managementmentioning

confidence: 99%

Quantifying the effects of road width on roadside vegetation and soil conditions in forests

et al. 2019

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Context The majority of ecological studies of roads have focused on their deleterious effects, and these preconceptions have hampered a full evaluation of the ecological functions of roads. As an integrated indicator, road width represents comprehensive effects, including anthropogenic and natural disturbances. Objectives We try to explore the different effects produced by various road widths by considering changes in forest vegetation and soil. Methods We selected six study forests spanning from Shandong Province in the north to Guangdong Province in the south of China, and we assessed the influences of wide and narrow roads on plant species diversity, biomass, and soil properties along transects running from the forest edges and adjacent forest interior. We used a ''shape-dependent model'' to explain the factors that determine the magnitude of road effects on forests. Results Three variables measured in this study changed significantly with increasing distance from the road to the forest interior along wide roads: tree biomass, herbaceous plant biomass, and soil pH. However, no measurable biological or environmental effects were found from narrow roads. The different shapes of glades in a forest may be one reason for the various effects caused by roads of different widths. Conclusions Forest roads of different widths may have quite different ecological effects. While wider roads with large glades tend to have substantial negative impacts, small-enough roads may cause little disturbance to the forest. This suggests that not all forest roads should be perceived as the same, and narrow roads may be compatible with forest conservation.

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Section: Implications For Forest Road Managementmentioning

confidence: 99%

Quantifying the effects of road width on roadside vegetation and soil conditions in forests

et al. 2019

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“…These populations may have been extinguished near roads with great urbanization, suffering over long-time high rates of road-kills [24]. Although the number of road-killed animals on roads is highly variable within roads, seasons, and days [26,73], the low number of road-killed amphibians found in this study is somewhat alarming.…”

Section: Discussionmentioning

confidence: 63%

“…We highlighted the main steps of the proposed research in Figure 1. There is still no scientific consensus on what modeling method may better support conservation actions, namely, to prioritize road segments for the establishment of There is still no scientific consensus on what modeling method may better support conservation actions, namely, to prioritize road segments for the establishment of mitigation measures or to organize more intensive monitoring on those segments [26]. Our study shows an alternative technique for modeling road-kills, as well as for exploring the data along the roads.…”

Section: Introductionmentioning

confidence: 96%

“…Road-kills are frequently very difficult to model, due to the excessive number of zeros and the clustered distribution of mortality events [25]. Many different methodologies have been applied to model the relationship between environmental features and the number of road-kills, such as logistic regression and ecological niche modeling [9,16,22,26,27]. These analyses often involve non-spatial modeling methods such as generalized linear models (e.g., [28]).…”

Section: Introductionmentioning

confidence: 99%

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A Spatial Approach for Modeling Amphibian Road-Kills: Comparison of Regression Techniques

Sousa‐Guedes

Franch

Sillero

2021

IJGI

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Road networks are the main source of mortality for many species. Amphibians, which are in global decline, are the most road-killed fauna group, due to their activity patterns and preferred habitats. Many different methodologies have been applied in modeling the relationship between environment and road-kills events, such as logistic regression. Here, we compared the performance of five regression techniques to relate amphibians’ road-kill frequency to environmental variables. For this, we surveyed three country roads in northern Portugal in search of road-killed amphibians. To explain the presence of road-kills, we selected a set of environmental variables important for the presence of amphibians and the occurrence of road-kills. We compared the performances of five modeling techniques: (i) generalized linear models, (ii) generalized additive models, (iii) random forest, (iv) boosted regression trees, and (v) geographically weighted regression. The boosted regression trees and geographically weighted regression techniques performed the best, with a percentage of deviance explained between 61.8% and 76.6% and between 55.3% and 66.7%, respectively. Moreover, the geographically weighted regression showed a great advantage over the other techniques, as it allows mapping local parameter coefficients as well as local model performance (pseudo-R2). The results suggest that geographically weighted regression is a useful tool for road-kill modeling, as well as to better visualize and map the spatial variability of the models.

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“…The energetic landscape is commonly estimated using measurements of overall dynamic body acceleration with miniature acceleration dataloggers attached to individuals in tracking studies (Halsey et al 2009, Gleiss et al 2011, Pagano et al 2020), these can be calibrated using physiological measures of energy expenditure from individuals in captivity (Mosser et al 2014, Pagano and Williams 2019). The spatial distribution of mortality risk can be estimated using habitat‐dependent mortality risk, which can be obtained from field experiments (Nowakowski et al 2015, Fletcher Jr. et al 2019), habitat‐dependent survival analysis (Low et al 2010, Basille et al 2013, Plante et al 2020) or mortality data, such as roadkill data (Zeller et al 2018, Lin et al 2019). Whether costs in terms of energy, mortality or both are most important for EC will depend upon the species and scope of the study (Table 1): energetic costs are likely to dominate connectivity at smaller scales (such as within an individual's home range), whereas connectivity at larger scales (such as dispersal between areas) are probably more affected by mortality costs.…”

Section: Step 1 Estimation Of Habitat Permeability: Environment–intrinsic Stepmentioning

confidence: 99%

Defining and quantifying effective connectivity of landscapes for species' movements

et al. 2021

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Ecosystem functioning depends on multiple successful interactions, many supported by individual movements. The degree to which the landscape allows these interactions to take place has been referred to as 'effective connectivity' (EC). Many of the cumulative impacts of anthropogenic activities on ecosystem functioning arise from changes in EC. Therefore, a coherent framework to quantify EC is urgently needed. Recent theoretical developments propose that studying EC requires the simultaneous consideration of spatial, environmental and species intrinsic characteristics (SEI framework).In this paper we further expand the SEI framework by integrating advances in geographic information science, ecological niche modelling, movement ecology, island biogeography and network sciences to develop a comprehensive three-step methodological approach for quantifying EC. First, using niche modelling and movement ecology, we quantify the species movement probabilities with respect to local environmental conditions. Second, we quantify ecological distances between non-adjacent locations by integrating species movement responses to the local environment with the spatial configuration of the landscape using the expected cost obtained from the randomized shortest paths (RSP) framework. This expected cost generalizes the two most frequently used ecological distance metrics, i.e. least-cost distance and resistance distance. Moreover the 'absorbing random walk' properties of RSP allow the integration of new developments in connectivity research, i.e. spatial absorbing Markov chains, to account for movement-related mortality. Third, drawing from island biogeography and metapopulation ecology, we scale ecological distances by relevant speciesand area-specific parameters to estimate EC for the ecological process of interest, e.g. migration, dispersal or gene flow.The integrative and highly interdisciplinary approach we propose can lead to increasingly more realistic measures of EC at different organizational levels. Moreover efficient computation allows its application to large-scale high-resolution landscapes for theoretical studies, conservation planning and sustainable management of real landscapes.

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Spatiotemporal identification of roadkill probability and systematic conservation planning

Cited by 23 publications

References 72 publications

Quantifying the effects of road width on roadside vegetation and soil conditions in forests

Quantifying the effects of road width on roadside vegetation and soil conditions in forests

A Spatial Approach for Modeling Amphibian Road-Kills: Comparison of Regression Techniques

Defining and quantifying effective connectivity of landscapes for species' movements

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